Addressing racial and ethnic disparities in premature exits from permanent supportive housing among residents with substance use disorders

Background. Permanent supportive housing (PSH) is an evidence-based practice for reducing homelessness that subsidizes permanent, independent housing and provides case management—including linkages to health services. Substance use disorders (SUDs) are common contributing factors towards premature, unwanted (“negative”) PSH exits; little is known about racial/ethnic differences in negative PSH exits among residents with SUDs. Within the nation’s largest PSH program at the Department of Veterans Affairs (VA), we examined relationships among SUDs and negative PSH exits (for up to five years post-PSH move-in) across racial/ethnic subgroups. Methods. We used VA administrative data to identify a cohort of homeless-experienced Veterans (HEVs) (n = 2,712) who were housed through VA Greater Los Angeles’ PSH program from 2016–2019. We analyzed negative PSH exits by HEVs with and without SUDs across racial/ethnic subgroups (i.e., African American/Black, Non-Hispanic White, Hispanic/Latino, and Other/Mixed [Asian, American Indian or Alaskan Native, and Native Hawaiian or Other Pacific Islander, and multi-race]) in controlled models and accounting for competing risk of death. Results. In competing risk models, HEVs with at least one SUD had 1.3 times the hazard of negative PSH exits compared to those without SUDs (95% CI: 1.00, 1.61). When stratifying by race/ethnicity, Other/Mixed race residents with at least one SUD had 6.4 times the hazard of negative PSH exits compared to their peers without SUDs (95% CI: 1.61–25.50). Hispanic/Latino residents with at least one SUD had 1.9 times the hazard compared to those without SUDs, also indicating a strong relationship with negative PSH exits; however, this association was not statistically significant (95% CI: 0.85–4.37). Black residents with at least one SUD had 1.2 times the hazard compared to those without SUDs (95% CI: 0.85–1.64), indicating no evidence of an association with negative PSH exits. Similarly, Non-Hispanic White residents with at least one SUD had 1.1 times the hazard compared to those without SUDs (95% CI: 0.75–1.66). Conclusions. These findings suggest relationships between SUDs and negative PSH exits differ between race/ethnic groups and suggest there may be value in culturally specific tailoring and implementation of SUD services for these subgroups.


BACKGROUND
Permanent supportive housing (PSH), which combines subsidies for permanent and independent housing with eld-based supportive services, is an evidence-based practice that addresses homelessness and its profound associated health and social disparities.PSH has demonstrated success in retaining homeless-experienced residents for up to two years, including those with substance use disorders (SUDs) (1)(2)(3); however, SUDs are also one of the most signi cant contributing factors towards premature, unwanted ("negative") PSH exits (e.g., eviction) and returns to homelessness (4)(5)(6).
The prevalence of SUDs varies across racial/ethnic subgroups, with increased prevalence among PSH residents who self-identify as racial/ethnic minorities compared to Non-Hispanic White residents who have experienced homelessness (7).As such, there is a need to identify subpopulations of homelessexperienced residents with heightened vulnerabilities towards negative PSH exits and to provide these groups with supports that enhance equity in housing stabilization interventions.
Developed in the early 1990s, PSH draws upon principles of "Housing First," providing affordable, lowbarrier housing options to individuals experiencing homelessness, and accompanied by linkages to medical and mental health services.PSH case management and other eld-based supportive services are guided by a harm-reduction approach, and do not mandate SUD treatment and/or sobriety (8-10).
There is substantial evidence that PSH reduces homelessness and increases housing stability for residents with SUDs (1,2).However, despite the effectiveness of the PSH model for residents with SUDs, substance use remains one of the most signi cant contributing factors towards housing instability (11), including within PSH programs (4,6).
In partnership with the Department of Housing and Urban Development (HUD), the Department of Veterans Affairs' (VA) Supportive Housing (HUD-VASH) program is the nation's largest PSH initiative and a useful setting to examine disparities in PSH outcomes and inform improvement efforts.SUDs are highly prevalent among homeless-experienced Veterans (HEVs), estimated to have 60-76% prevalence (12,13) compared to 11-18% among the general Veteran population (14,15).Moreover, relative to the general Veteran population, HEVs have greater racial/ethnic diversity (16,17) and diversity among Veterans is only projected to increase in coming years (18).As such, there is a need to assess racial/ethnic disparities in PSH outcomes to inform tailored and targeted strategies for mitigating these disparities and to ensure the provision of equitable VA medical care and social services across subgroups of HEV residents.
Existing literature has identi ed disparities in SUD diagnoses among Veterans who self-identify as racial/ethnic minoritized groups, including the underdiagnosis of SUDs among Hispanic/Latino Veterans (19).Existing literature has also identi ed higher odds of housing instability among Veterans who selfidentify as racial/ethnic minoritized groups (17).However, we know little about the relationships between SUDs and PSH outcomes across racial/ethnic subgroups of Veterans.An analysis of PSH outcomes from the rst wave of HUD-VASH voucher administration found HUD-VASH to be more effective in improving housing retention outcomes among Non-Hispanic White HEVs with SUDs compared to African American/Black HEVs with SUDs; this analysis speci cally noted that interactions between SUDs and race/ethnicity were deserving of future study (20).The interactions among SUDs, race/ethnicity, and housing outcomes in this context remain understudied.To ll this gap, among a cohort of HEVs housed through HUD-VASH in Los Angeles, we used administrative data to examine the relationships between SUDs and negative PSH exits, overall and by race/ethnicity, for up to ve years post-PSH entry.

Sample and procedures
We used VA administrative data (from the Corporate Data Warehouse, CDW) and VA's homeless registry (the Homeless Operations Management and Evaluation System, HOMES) to identify a cohort of HEVs (n = 2,933) housed through HUD-VASH at VA Greater Los Angeles between 2016-2019.VA Greater Los Angeles' HUD-VASH program is the largest of any VA facility in the nation.In addition to nancial subsidies for permanent housing, HUD-VASH provides eld-based case management that includes linkages to medical and behavioral health services within and outside VA, including SUD treatment.We retrospectively captured housing information up to ve-years post HEVs' move-in date to PSH (i.e., through December 31, 2021).
To identify our analytic sample, we abstracted residents' HUD-VASH records from HOMES, including information from case managers about move-in dates, retention in PSH, and HUD-VASH exits, when applicable.Though some residents exit HUD-VASH for positive reasons (e.g., income increases typically attributed to employment or disability claim attainment, relocation to other permanent housing) most residents who exit HUD-VASH case management do so for negative reasons (e.g., eviction, incarceration, or returns to homelessness).From the 2,933 HEVs who moved into Los-Angeles-based HUD-VASH in 2016-2019, we used CDW to exclude persons with missing data on key variables of interest, including race/ethnicity (n = 177) and marital status (n = 18).Those with "Other" marked as their reason for PSH exit (n = 26) were also treated as missing.Our nal analytic sample included 2,712 residents.We retrospectively captured time between each resident's PSH move-in date and the event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]).These data were originally abstracted for a project examining smoking behavior and housing outcomes among this cohort of HEVs.All study procedures were reviewed and approved by VA Greater Los Angeles' Institutional Review Board as constituting quality improvement.

Conceptual framework
Measure selection and analyses were guided by the Behavioral Model for Vulnerable Populations (21) which describes person-level factors that predispose residents to health and housing outcomes (including age, gender, race/ethnicity, and marital status), which interact with characteristics that enable health access (e.g., primary care empanelment), needs (here, evaluated need for medical and mental health care), and health behaviors (e.g., primary care utilization) to in uence HUD-VASH outcomes (retention or positive exits versus negative exits) (Fig. 1).

Predisposing factors
Demographic variables included age (modeled as a continuous variable at the time of move-in); gender (men and women); and marital status (strati ed as married, previously married, or never married at the time of move-in) (11,14).
For our key predisposing factor of interest, used to stratify the sample, we drew from VA administrative data which captures race across the following categories: African American or Black; White; Asian; American Indian or Alaskan Native (AIAN); Native Hawaiian or Other Paci c Islander (NHPI); Other Race; or Unknown.Ethnicity, a separate measure, captured Veterans identi ed as "Hispanic or Latino" versus "Not Hispanic or Latino".To create a combined measure of race and ethnicity, we identi ed White and African American/Black patients who were not of Hispanic or Latino ethnicity, labelling these residents as Non-Hispanic White and African American/Black, respectively.We collapsed residents of White race and Hispanic/Latino ethnicity into a "Hispanic/Latino" category.Residents who identi ed as a race other than White but with Hispanic ethnicity (e.g., African American/Black race and Hispanic/Latino ethnicity) were coded as "Other/Mixed."Asian, AIAN, and NHPI, and multi-racial Veterans were combined into the Other/Mixed category due to small sample sizes.

Enabling factors
We drew from the administrative data to determine Veterans empaneled to primary care, coined "Patient Aligned Care Teams" (PACTs), the VA's patient-centered medical home model.We included Veterans assigned to specialty PACTS (e.g., Homeless-PACT [H-PACT] with providers and services tailored to HEVs) as empaneled.Primary care empanelment was modeled as a binary variable at the time of PSH move-in.

Need factors
Need factors were determined using diagnoses captured by primary or secondary International Classi cation of Disease, Tenth Revision (ICD-10) codes associated with VA outpatient or inpatient encounters in the administrative data over the two years prior to PSH move-in.ICD-10 codes associated with diagnoses are available in the supplemental materials.Mental health diagnoses included in these analyses included schizophrenia and other psychotic disorders, bipolar disorders, post-traumatic stress disorder (PTSD), depressive disorders (e.g., major depression, dysthymia), and anxiety disorders (e.g., panic disorder, generalized anxiety disorder, social anxiety).Binary indicators for each mental health diagnosis re ect the presence of a visit for the given diagnosis versus the absence of a visit for the diagnosis.For mental health diagnoses, we modeled diagnoses separately due to their distinct relationships with housing retention as identi ed in prior literature (4,22,23).Physical health diagnoses were ascertained via the Elixhauser Comorbidity Index Score (24), altered to exclude diagnoses already adjusted for in the study model (i.e., mental health diagnoses and substance use disorders).
For our key predictor variable of interest, we were focused on the presence or absence of SUD diagnoses, which we de ned to encompass alcohol use disorder or any drug use disorder (including opioids, cannabis, sedatives/hypnotics or anxiolytics, cocaine, other stimulants, hallucinogens, inhalants, and other psychoactive substances).

Health Behaviors
Using administrative data, we characterized primary care utilization as the health behavior of interest.We captured primary care engagement in one-year post-PSH move-in, modeled as a binary variable (at least one primary care visit, yes or no).

Housing outcomes
Our outcome of interest was housing retention, which was captured through retention or exit from HUD-VASH PSH.Among residents who exited housing, their exit date was recorded along with a reason for exit in HOMES by case managers.We note that some residents in HUD-VASH exit rental units but remain enrolled in the program; we did not obtain that data, which is not available within VA's homeless registry.
Residents who were deemed to have negatively exited were con rmed by housing arrangement information (i.e., place not meant for habitation, transitional housing, shelter, treatment facility, or other temporary tenure), upon exit entered by case managers with the corresponding exit date.In the case of residents whose housing arrangement information was unknown, they were considered to have exited housing and presumed to have returned to homeless as the case manager could not locate them to determine their housing arrangement.
We strati ed housing retention as: 1) retained (i.e., still housed at end of observation period; this included Veterans who exited the HUD-VASH program due to accomplishment of case management goals and/or no longer had need for case management and supportive services but remained housed; 2) positive or neutral exits; and 3) negative exits.We classi ed HUD-VASH exits as positive or neutral if they were associated with the following exit reasons: Veteran found/chose other housing; was no longer nancially eligible for housing voucher (i.e., income was higher than eligible income rates); was escalated to a higher level of care; or was transferred to another HUD-VASH unit, e.g., in a different city or state.We classi ed negative exits as those attributed to other exit reasons, including: the Veteran cannot be located; did not comply with case management; was incarcerated; was no longer interested in participating in HUD-VASH; was unhappy with HUD-VASH housing; or was evicted and/or had other housing related issues or problems.
The outcome of interest was dichotomized ("Yes" or No") as experienced a negative PSH exit versus the absence of a negative exit (i.e., a positive or neutral PSH exit or retained housing).Of note, we also used VA administrative data to identify residents who became deceased over the study period as opposed to exiting for other reasons, as this is a competing event (i.e., precludes the resident from exiting PSH during the study period).

Time-to-event
Each HEV was retrospectively followed beginning with their PSH move-in date and ending with either of the following events: the outcome of interest (i.e., PSH exit), a competing event (i.e., death), or the end of the study follow-up period (i.e., December 31, 2021)-whichever occurred rst.We then calculated the time (in days) between each resident's PSH move-in date and their respective event.

Analyses
We characterized predisposing, enabling, and need factors among HEVs with and without SUDs.We did not compare exposed and unexposed groups or include inferential statistics (e.g., p-values) per the STROBE guidelines (25).We retrospectively captured time between each resident's PSH move-in date and the event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [ December 31, 2021]).Time-to-event (with PSH exit serving as "event") data were used to calculate incidence rates.This was followed by survival analyses, using hazard functions, to compare occurrence of negative PSH exits among HEVs with SUDs versus those with no SUDs.
The proportional hazards assumption (i.e., that the relative hazards remain constant over time), which is the fundamental assumption for hazard regressions, was tested to examine if the effects of SUDs on negative PSH housing exits varied over time.In addition, we further tested the proportional hazards assumption to examine if the effects of SUDs on negative PSH housing exits varied over time within each racial/ethnic group.The proportional hazards assumption was not violated in any racial/ethnic group.
The reported incidence rates do not account for the competing risk of death.Therefore, to account for the competing risk of death in survival analyses, we t Fine-Gray subdistribution hazard models, as this approach estimates hazards over time in the presence of competing events (26).We estimated hazard ratios and 95% con dence intervals (95% CIs) for negative PSH exits comparing HEVs with SUDs to those without SUDs, accounting for the competing risk of death in multivariable models, and controlling for all other predisposing (age, gender, marital status), need (mental health diagnoses and Elixhauser score), enabling (primary care empanelment), and health behavior (primary care engagement) factors.These models were strati ed across the four racial/ethnic subgroups to examine if the relationship between SUDs and negative PSH exits varied by racial/ethnicity.All analyses were conducted using Base SAS 9.4 ©. b Includes Asian, American Indian/Alaskan Native, Native Hawaiian/Paci c Islander, Other Race, and Multi-racial/ethnic c Elixhauser Comorbidity Index Score is a measure of overall severity of comorbidities.The higher the score, the higher the comorbidities.In this analysis, the substance use and mental health diagnoses were removed from Elixhauser calculations to avoid over adjusting for SUDs and mental health diagnoses.

Sample characteristics
Table 1 displays differences in needs and housing outcomes between HEVs with at least one SUD and HEVs without SUDs.HEVs with SUDs were more likely to be diagnosed with PTSD (48%), schizophrenia or other psychotic disorders (22%), bipolar disorders (14%), depressive disorders (57%), and anxiety disorders (30%) compared to HEVs without SUDs (18%; 7%; 4%; 21%; and 13% respectively).In addition, HEVs with SUDs had a higher mean number of physical health comorbidities compared to HEVs without SUDs (average Elixhauser score of 2.7 versus 1.8).HEVs with SUDs were also more likely to have at least one primary care visit within one year of PSH move-in date (86%) compared to HEVs without SUDs (67%).HEVs with SUDs also had a higher proportion of negative PSH exits (17% versus 13%) and a higher proportion of deaths (10% versus 7%), compared to those without SUDs.

Associations between substance use disorders and negative PSH exits
The incidence of negative housing exits was slightly higher among HEVs with SUDs than in the group with no SUDs (incidence per 1,000 person-years = 56.6 vs. 42.2).HEVs with at least one SUD had 1.26 times the hazard of negative PSH exits compared to those without SUDs (cHR Overall = 1.29; 95% CI = 1.06, 1.57).After controlling for predisposing, need, and enabling factors, the hazard ratio did not change materially (aHR Overall =1.27; 95% CI = 1.00, 1.61; see Table 2).Other/Mixed race HEVs with at least one SUD had 6.4 times the risk of negative PSH exits compared to their peers without SUDs (aHR Other/Mixed = 6.41, 95% CI: 1.61-25.50),whereas associations between SUDs and negative PSH exits were not statistically signi cant among Black and Non-Hispanic White HEVs (aHR Black =1.18, 95% CI: 0.85-1.64;aHR White =1.12, 95% CI: 0.75-1.66)(Fig. 2).Hispanic/Latino HEVs with at least one SUD had 1.9 the hazard compared to those without SUD; however, this association was not statistically signi cant (aHR Hisp/Latino =1.92, 95% CI: 0.85-4.37).

DISCUSSION
We examined the relationships between SUDs and housing outcomes across racial/ethnic subgroups in a cohort of Veterans housed in HUD-VASH in Los Angeles.We identi ed an overall association between SUDs and negative PSH exits.However, in analyses strati ed by race and ethnicity, we found this association varied by race/ethnic group.There was no statistically signi cant association between SUDs and negative PSH exits for Black, Non-Hispanic White, and Hispanic/Latino residents.Though it did not reach statistical signi cance, for residents of Hispanic/Latino ethnicity, the effect of presence of SUDs on negative PSH exits was nearly double that of White and Black subgroups.We observed a statistically signi cant positive association for Other/Mixed race HEVs.Notably, the relationships between SUDs and negative PSH exits were much stronger among Other/Mixed HEVs compared to other racial/ethnic groups, although this group comprises a small subset of (4%).
Our ndings differ from prior studies that broadly examined SUDs as associated with increased rates of premature or unwanted exits from PSH but did not focus on race/ethnic differences (6).In these data, among most racial/ethnic subgroups, the effects of SUDs on negative PSH exits were not signi cant, which suggests that current strategies to retain residents with SUDs in PSH, (e.g., improving timely access to supportive services, including behavioral health care) may be effective among these subgroups (4).However, despite these efforts however, our analyses highlight potentially important disparities in PSH housing outcomes among Hispanic/Latino and Other/Mixed race PSH residents with SUDs.
Among Hispanic/Latino and Other/Mixed race residents, disparities in health behaviors, including SUD service utilization, may contribute to the increased effect of SUDs on negative PSH exits.In prior literature, Veterans of Hispanic/Latino and Other/Mixed race/ethnicity were found to have SUD prevalence rates nearly two times that of clinically documented SUD (19).Further indicating a gap in VA treatment receipt for SUD among these minoritized groups, White Veterans diagnosed with SUDs were found to be much more likely to receive treatment for SUD diagnoses as compared to Hispanic/Latino Veterans diagnosed with SUDs (27).This trend is also seen among Asian and NHPI populations.Across the general population, outside of Veteran-speci c literature, minoritized communities have been shown to severely underutilize SUD treatment.Underutilization among these populations is often attributed to barriers to access including stigma, cost, lack of knowledge, and cultural attitudes (28).We suspect that tailored implementation approaches designed to increase adoption of evidence-based SUD treatments dissemination within VA (e.g., using peers to activate HEVs from racial/ethnic minoritized groups) may address these disparities and increase health equity within the PSH program.
Prior research has found that other potentially relevant factors in examining relationships between SUDs and negative PSH exits include socioeconomic disparities associated with developing SUDs (29), differential stigma associated with speci c substance use (30), and other social factors associated with SUDs (e.g., disparate marketing for substances in low-income and minority communities [31]).Racial/ethnic minority Veterans are also noted to have an increased risk of adverse SUD and psychiatric treatment outcomes (e.g., involuntary hospitalizations, shorter treatment duration) compared to their Non-Hispanic White peers (32).In general, researchers have attributed increased risk of SUDs among racial/ethnic minority populations to differential access to health services, social supports, and other healthy coping mechanisms (e.g., professional/clinic services, social service resources, community infrastructure).We note that, in this study, these disparities may be mitigated in part by the VA infrastructure; during the study period, all HUD-VASH residents were eligible for VA healthcare which awarded them equitable potential access to all health services, including SUD treatment.

Strengths and limitations
The primary strength of this study is its ability to examine longitudinal data for a large subset of PSH enrollees in a system that integrates housing and health services.VA administrative and homeless registry data provides robust information related to diagnoses, date of housing move-in, exits from PSH enrollment, and the competing risk of death.
This study also had limitations.First, misclassi cation of PSH exits (i.e., negative, positive, neutral) may have occurred.Each exit is categorized using standardized reasons for exit which omit granular details about factors contributing to each participant's PSH exit.Second, while there a large sample size for the entire cohort, when stratifying by race/ethnicity, small proportions in some subgroups (i.e., Asian, AIAN, NHPI, Other, and Mixed) necessitated collapsing of these subgroups into one category ("Other/Mixed") which comprised 4% of HEVs.Future studies with larger samples sizes and/or utilizing qualitative methods could help provide greater insights into the potential vulnerabilities of racial/ethnic subgroups with smaller populations.Third, these analyses were based on diagnosed and documented SUDs, which may vary by race/ethnicity.In addition, in this study, we combined all diagnoses of substance use disorders within the relevant time frame (two years prior to housing move-in).Future research would bene t from examining differences in housing retention associated with speci c substances used.We note speci c complexities in data interpretation related to persons who only had cannabis use disorder to classify them as having a SUD; cannabis was legalized in the state of California in 2016, including at the study site (33).Fourth, it is possible that the high rates of comorbid mental health disorders and SUDs among this population overshadowed effects of SUDs on negative PSH exits.Future studies may bene t from assessing the relationships between comorbid mental health and SUD diagnoses on housing outcomes.Last, as a study conducted with one large and urban VA, it is unclear how much our ndings extrapolate to a national HUD-VASH sample, or to homeless-experienced consumers who receive PSH services or health services outside the VA.

CONCLUSIONS
This study suggests that speci c race/ethnicity groups largely explain the associations between SUDs and negative PSH exits, with the relationship between SUDs and negative PSH exits being much stronger among Other/Mixed HEVs and trending towards signi cance among Hispanic/Latino HEVs as compared to PSH residents of other race/ethnicity groups.PSH programs and providers should consider potential heightened vulnerabilities for negative housing outcomes among minoritized residents, particularly those of Hispanic/Latino, Asian, NHPI, AIAN, and Other/Mixed race and ethnicity.These ndings would bene t from integration with qualitative data that explores potential reasons for differential rates for negative exits among PSH residents of different race/ethnicity groups.Such research could inform culturally-speci c tailoring of SUD services and implementation strategies that support equitable use of SUD services within these subgroups, which ultimately have potential to reduce Veteran homelessness and increase health equity.

Figures
Figure 1 Conceptual framework adapted from the Behavioral Model for Vulnerable Populations (Gelberg, et al., 2000).

Table 1
describes the analytic sample (n = 2,712).Of the sample, 50% were Black, 33% were Non-Hispanic White, 12% were Hispanic/Latino, 4% were Other/Mixed, and 40% had at least one SUD (Table1).A majority (90%) of HEVs in the cohort were male and 88% were not married.The mean age at program entry was 53.4 ± 13.6 years.The average follow-up time (i.e., the average time between PSH move-in date and event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]) was 3.0 years among HEVs with SUDs and 3.
a Average time between PSH move-in date and event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021])

Table 2
Hazard ratio of negative permanent supportive housing (PSH) exits according to substance use disorder and race/ethnicity (N = 2712) b Using a Fine and Gray competing risk analysis c Adjusting for gender, age, marital status, physical health diagnoses (Elixhauser Comorbidity Index Score), mental health diagnoses (PTSD, schizophrenia, bipolar, depression, and anxiety disorder) d Includes Asian, AIAN, NHPI, Other Race, and Multi-Racial/Ethnic Strati cation by race/ethnicity

Table 2
also presents hazard ratios negative PSH exits by SUD status, strati ed by race/ethnicity.